Gravitropism in the rhizoids of the alga Chara: a model system for microgravity research
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NASA Discipline Plant Biology↗
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Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data
Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.
James Andrew Casaletto↗
Design and Impacts of Land-Biogenic-Atmosphere Coupling in the NASA-Unified WRF (NU-WRF) Modeling System
Land-Atmosphere coupling is typically designed and implemented independently for physical (e.g. water and energy) and chemical (e.g. biogenic emissions and surface depositions)-based models and applications. Differences in scale, data requirements, and physics thus limit the ability of Earth System models to be fully coupled in a consistent manner. In order for the physical-chemical-biological coupling to be complete, treatment of the land in terms of surface classification, condition, fluxes, and emissions must be considered simultaneously and coherently across all components. In this study, we investigate a coupling strategy for the NASA-Unified Weather Research and Forecasting (NU-WRF) model that incorporates the traditionally disparate fluxes of water and energy through NASA's LIS (Land Information System) and biogenic emissions through BEIS (Biogenic Emissions Inventory System) and MEGAN (Model of Emissions of Gases and Aerosols from Nature) into the atmosphere. In doing so, inconsistencies across model inputs and parameter data are resolved such that the emissions from a particular plant species are consistent with the heat and moisture fluxes calculated for that land cover type. In turn, the response of the atmospheric turbulence and mixing in the planetary boundary layer (PBL) acts on the identical surface type, fluxes, and emissions for each. In addition, the coupling of dust emission within the NU-WRF system is performed in order to ensure consistency and to maximize the benefit of high-resolution land representation in LIS. The impacts of those self-consistent components on' the simulation of atmospheric aerosols are then evaluated through the WRF-Chem-GOCART (Goddard Chemistry Aerosol Radiation and Transport) model. Overall, this ambitious project highlights the current difficulties and future potential of fully coupled. components. in Earth System models, and underscores the importance of the iLEAPS community in supporting improved knowledge of processes and innovative approaches for models and observations.
Tan, Qian↗
Adaptation of SUBSTOR for controlled-environment potato production with elevated carbon dioxide
The SUBSTOR crop growth model was adapted for controlled-environment hydroponic production of potato (Solanum tuberosum L. cv. Norland) under elevated atmospheric carbon dioxide concentration. Adaptations included adjustment of input files to account for cultural differences between the field and controlled environments, calibration of genetic coefficients, and adjustment of crop parameters including radiation use efficiency. Source code modifications were also performed to account for the absorption of light reflected from the surface below the crop canopy, an increased leaf senescence rate, a carbon (mass) balance to the model, and to modify the response of crop growth rate to elevated atmospheric carbon dioxide concentration. Adaptations were primarily based on growth and phenological data obtained from growth chamber experiments at Rutgers University (New Brunswick, N.J.) and from the modeling literature. Modified-SUBSTOR predictions were compared with data from Kennedy Space Center's Biomass Production Chamber for verification. Results show that, with further development, modified-SUBSTOR will be a useful tool for analysis and optimization of potato growth in controlled environments.
NASA Discipline Life Support Systems↗
USSR Space Life Sciences Digest, issue 28
This is the twenty-eighth issue of NASA's Space Life Sciences Digest. It contains abstracts of 60 journal papers or book chapters published in Russian and of 3 Soviet monographs. Selected abstracts are illustrated with figures and tables from the original. The abstracts in this issue have been identified as relevant to 20 areas of space biology and medicine. These areas include: adaptation, aviation medicine, botany, cardiovascular and respiratory systems, developmental biology, endocrinology, enzymology, equipment and instrumentation, hematology, human performance, immunology, life support systems, mathematical modeling, musculoskeletal system, neurophysiology, personnel selection, psychology, radiobiology, reproductive system, and space medicine.
Stone, Lydia Razran↗
Lunar and Planetary Science XXXV: Impacts on Mars and Earth
The session "Impacts on Mars and Earth" included the following reports:A Timescale for Major Events in Early Mars Crustal Evolution; Impact Craters in Arabia Terra, Mars; Ancient Giant Basin/Aquifer System in the Arabia Region, Mars; Small Crater Morphology Within Gusev Crater and Isidis Planitia: Evidence for Widespread Secondaries on Mars; Martian Surface Properties: Inferences from Resolved Differences in Crater Geometries; Asymmetric Ramparts of Secondary Craters in Cerberus, Mars: Interpretation and Implications for Local Stratigraphy; Depth to Diameter Relationships of Craters in the High Latitudes (70 -80 ) of Mars: Implications for Geologic History of Those Areas; Impact Cratering on Mars: Search for Target Influence on Morphology; Starting Conditions for Hydrothermal Systems Underneath Martian Craters: Hydrocode Modeling; Impact-induced Hydrothermal System at the Sudbury Crater: Duration, Temperatures, Mechanics, and Biological Implications; Major and Trace Element Variations in Impact Crater Clay from Chicxulub, Lonar, and Mistastin, Implications for the Martian Soil; Stable Isotopes and Hydrothermal Fluid Source in the Yaxcopoil-1 Borehole, Chicxulub Impact Structure, Mexico; and Chicxulub Impact Melts: Geochemical Signatures of Target Lithology Mixing and Post-Impact Hydrothermal Fluid Processes.
Source record↗
Open Science for Plants in Space: Improvements in NASA's Open Science Data Repository
Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, elevated CO2, and many other abiotic stressors. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. Current OSDR standards include the ISA (Investigation-Study-Assay) experiment model, assay metadata configurations, and standardized terminology and ontologies. In 2024 OSDR will include a new suite of features for improved FAIR compliance including downloadable plant metadata templates, data submission tools and overall improved AI-readiness of plant datasets. AI/ML methods can be helpful tools to overcome the inherent challenges of space biology research (small sample size, sparse and heterogeneous data etc.). However these methods are built on an assumption of normalized and well-curated data. OSDR’s new curation tools will improve users ability to leverage ML and AI methods to model space biology data and better understand the complex effects of spaceflight on living systems across hierarchical biological levels. We look forward to sharing our advances with the spaceflight community.
FAIR↗
Space Shuttle food galley design concept
A food galley has been designed for the crew compartment of the NASA Space Shuttle Orbiter. The rationale for the definition of this design was based upon assignment of priorities to each functional element of the total food system. Principle priority categories were assigned in the following order: food quality, nutrition, food packaging, menu acceptance, meal preparation efficiency, total system weight, total system volume, and total power requirements. Hence, the galley was designed using an 'inside-out' approach which first considered the food and related biological functions and subsequently proceeded 'outward' from the food to encompass supporting hardware. The resulting galley is an optimal design incorporating appropriate priorities for trade-offs between biological and engineering constraints. This design approach is offered as a model for the design of life support systems.
Heidelbaugh, N. D.↗
A Cell Kinetic Model of Granulocytopoiesis Under Radiation Exposure: Extension from Murines to Canines and Humans
Space radiation poses significant challenges to space travel, and it is essential to understand the possible adverse effects from space radiation exposure to the radiosensitive organ systems that are important for immediate survival of human, e.g., the hematopoietic system. In this presentation a biomathematical model of granulocytopoiesis is described and used to analyze the blood granulocyte changes seen in the blood of mammalians under continuous and acute radiation exposure. This is one of a set of hematopoietic models that have been successfully utilized to simulate and interpret the experimental data of acute and chronic radiation on rodents. We discuss the underlying implicit regulation mechanism and the biological relevance of the kinetic parameters estimation method. Extension of the model to predictions in dogs and humans systems indicates that the modeling results are consistent with the cumulative experimental and empirical data from various sources. This implies the potential to integrate the models into one united system for monitoring the hematopoietic response of various species under irradiation. Based on the evidence of threshold responses of dogs to extended periods of low daily dose exposures, we discuss the potential health risks of the space traveler under chronic stress of low-dose irradiation and the possibly encountered Solar Particle Events.
Hu, Shaowen↗
Broader Impacts and Outreach Videos on Ocean Physics and Biogeochemistry
This is just a preliminary NF1676 in advance of filming for a project....A convergent team of experts in biology, chemistry, physics, engineering, mathematics, and computational modeling examine how dynamic and coupled phytoplankton-pathogen-particle-predator linkages coalesce to explain the observed high spatial variability in the efficiency of the export of particulate organic carbon (POC) to the deep ocean. They elucidate and quantify the linkages between viruses and ballast minerals to increase understanding of carbon cycling in the oceanic biological carbon pump and the impact of viruses within it. By providing knowledge than can be used to improve the parameterization of carbon export in Earth system models, the project will help reduce uncertainty in regional marine biogeochemical projections, potentially improving marine ecosystem and fisheries management on timescales from seasons to decades. The project includes activities that provide teaching resources and hands-on training to educators within a ‘Tools of Science’ program that provides a simple and succinct way to communicate the process of scientific research to students in a way that is useful to teachers. The project couples laboratory-based experiments on model host-virus-grazer systems with extensive field based observational and manipulative studies on natural populations of diatoms and coccolithophores, the two phytoplankton groups that account for most of the estimated particulate organic matter flux to the deep ocean. Experiments and measurements integrate diagnostic biological and chemical controls on infection and particle coagulation theory with microscale physics and grazing to quantify links to each hypothesized export mechanism under field-relevant turbulent conditions. Cutting-edge engineering and analytical tools are used to diagnose and track infection dynamics while characterizing and quantifying particle aggregation and disaggregation, mineral dissolution, sinking dynamics, grazing rates, and fecal pellet production at unprecedented resolution and under well-defined, microscale physical regimes. Field campaigns elucidate the relative efficiency of hypothesized mechanisms in stimulating POC export in natural blooms, while providing bulk and size-resolved estimates of POC flux.
Daniel B Whitt↗
Automated Knowledge Discovery From Simulators
A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.
Burl, Michael↗
Evolutionary Computational Methods for Identifying Emergent Behavior in Autonomous Systems
A technique based on Evolutionary Computational Methods (ECMs) was developed that allows for the automated optimization of complex computationally modeled systems, such as autonomous systems. The primary technology, which enables the ECM to find optimal solutions in complex search spaces, derives from evolutionary algorithms such as the genetic algorithm and differential evolution. These methods are based on biological processes, particularly genetics, and define an iterative process that evolves parameter sets into an optimum. Evolutionary computation is a method that operates on a population of existing computational-based engineering models (or simulators) and competes them using biologically inspired genetic operators on large parallel cluster computers. The result is the ability to automatically find design optimizations and trades, and thereby greatly amplify the role of the system engineer.
Terrile, Richard J.↗
Simulation of passive thermal behavior of a cooling biological system - Entry into hibernation
Thermal behavior simulation of cooling biological system, describing heat generation and transfer at normothermic to hibernating body temperatures with mathematical model
Gray, E. W.↗
User's instructions for the whole-body algorithms
The design of an algorithm that provides for the simulation of long and short term biological stresses is reported. The physiological responses of models representing circulatory, respiratory, cardiovascular, and thermoregulatory systems during space flight simulation are described.
Grounds, D. J.↗
Interactions of CO2, temperature and management practices: simulations with a modified version of CERES-Wheat
A new growth subroutine was developed for CERES-Wheat, a computer model of wheat (Triticum aestivum) growth and development. The new subroutine simulates canopy photosynthetic response to CO2 concentrations and light levels, and includes the effects of temperature on canopy light-use efficiency. Its performance was compared to the original CERES-Wheat V-2 10 in 30 different cases. Biomass and yield predictions of the two models were well correlated (correlation coefficient r > 0.95). As an application, summer growth of spring wheat was simulated at one site. Modeled crop responses to higher mean temperatures, different amounts of minimum and maximum warming, and doubled CO2 concentrations were compared to observations. The importance of irrigation and nitrogen fertilization in modulating the wheat crop climatic responses were also analyzed. Specifically, in agreement with observations, rainfed crops were found to be more sensitive to CO2 increases than irrigated ones. On the other hand, low nitrogen applications depressed the ability of the wheat crop to respond positively to CO2 increases. In general, the positive effects of high CO2 on grain yield were found to be almost completely counterbalanced by the negative effects of high temperatures. Depending on how temperature minima and maxima were increased, yield changes averaged across management practices ranged from -4% to 8%.
NASA Discipline Life Support Systems↗
Effects of microgravity on circadian rhythms in insects
The desert beetle Trigonoscelis gigas Reitt. was used as a biological model in studies that examined the effects of space flight on the circadian timing system. Results from studies aboard the Bion-10, Bion-11, and Photon-11 missions are reported. The control study is an ongoing Mir experiment. The studies indicate that the free-running period in beetles may be longer during space flight.
NASA Experiment Number 9401698↗
Nanoscale Bio-engineering Solutions for Space Exploration: The Nanopore Sequencer
Characterization of biological systems at the molecular level and extraction of essential information for nano-engineering design to guide the nano-fabrication of solid-state sensors and molecular identification devices is a computational challenge. The alpha hemolysin protein ion channel is used as a model system for structural analysis of nucleic acids like DNA. Applied voltage draws a DNA strand and surrounding ionic solution through the biological nanopore. The subunits in the DNA strand block ion flow by differing amounts. Atomistic scale simulations are employed using NASA supercomputers to study DNA translocation, with the aim to enhance single DNA subunit identification. Compared to protein channels, solid-state nanopores offer a better temporal control of the translocation of DNA and the possibility to easily tune its chemistry to increase the signal resolution. Potential applications for NASA missions, besides real-time genome sequencing include astronaut health, life detection and decoding of various genomes.
Stolc, Viktor↗
Nanoscale Bioengineering Solutions for Space Exploration the Nanopore Sequencer
Characterization of biological systems at the molecular level and extraction of essential information for nano-engineering design to guide the nano-fabrication of solid-state sensors and molecular identification devices is a computational challenge. The alpha hemolysin protein ion channel is used as a model system for structural analysis of nucleic acids like DNA. Applied voltage draws a DNA strand and surrounding ionic solution through the biological nanopore. The subunits in the DNA strand block ion flow by differing amounts. Atomistic scale simulations are employed using NASA supercomputers to study DNA translocation. with the aim to enhance single DNA subunit identification. Compared to protein channels, solid-state nanopores offer a better temporal control of the translocation of DNA and the possibility to easily tune its chemistry to increase the signal resolution. Potential applications for NASA missions, besides real-time genome sequencing include astronaut health, life detection and decoding of various genomes. http://phenomrph.arc.nasa.gov/index.php
Ioana, Cozmuta↗